Abstract
We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.
Highlights
The current COVID-19 pandemic is the main health crisis in the world in a century, with over 220 million cases and 4.5 million deaths [1]
A mobility or social distancing metric is compared to the growth rate of cases of COVID-19, or to the effective reproduction number Rt
The span of time of the data was chosen to avoid the effect of vaccination in the United States and Europe, while for Brazil detailed and publicly available anonymized data on each vaccine shot delivered allows modeling the time evolution of the pandemic for a longer period
Summary
The current COVID-19 pandemic is the main health crisis in the world in a century, with over 220 million cases and 4.5 million deaths [1]. In order to quantify and qualify the degree of social distancing and its effects, some different approaches have been proposed: by survey questionnaires in the population in order to assess adherence to social distancing and to compare it to the growth of cases, or deaths [12], or by using mobility data from different sources [13,14,15,16,17,18,19] In the latter case, a mobility or social distancing metric is compared to the growth rate of cases (or deaths) of COVID-19, or to the effective reproduction number Rt. As we discuss below, this introduces a limitation in the analysis due to the fact that the interpretation of both the growth rate and Rt at the beginning of the pandemic, when most of the population is still susceptible to the virus, is different to that at latter stages, when a non-negligible proportion of the population has already been infected, or has already been vaccinated. This explains the result by Gatalo et al [20] who obtained a strong Pearson correlation between phone mobility data and COVID-19 growth rates at earlier stages, but a weaker correlation at later stages, for 25 counties in the United States
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